Liu, Yangcheng. 2016. Using GIS to Analyze the Social Service Function of the Parks and Green Spaces in

Guilin, China. Volume 19. Papers in Resource Analysis. 9 pp. Saint Mary’s University of Minnesota Central

Services Press. Winona, MN. Retrieved (date) http://www.gis.smumn.edu.

Using GIS to Analyze the Social Service Function of the Parks and Green Spaces in

Guilin, China

Yangcheng Liu

Department of Resource Analysis, Saint Mary’s University of Minnesota, Winona, MN

55987

Keywords: GIS, Parks and Green Spaces, Social Service Function, Accessibility, Fairness

Abstract

Parks and green spaces are an indispensable part of the urban ecosystem. Besides their

ecological service functions, these open areas have an important role to play in society.

When evaluating their social service function, one should take into consideration

accessibility, equality, and access. To perform an assessment, with the help of GIS, this

study applied a set of evaluation metrics to review the parks and green spaces in Guilin,

China from four aspects: service coverage ratio, degree of service area overlap, service

area coverage in various residential land use types, and degree of service area overlap

within residential land use types. Results revealed 85.1% of residential land fell within the

green space service areas and these areas had equitable access as well. There was no

suggestion the proportion of the green space service area is higher where residential land is

comprised of more costly dwellings compared to the other residential types within the city.

Consequently, public green space areas in Guilin attain effective utilization, providing

sufficient availability to citizens.

Introduction

Population within cities continue to grow

as the urbanization process accelerates,

which inevitably results in more and more

severe environmental threats (Zhang and

Wang, 2001). Pollution, including air,

water, solid waste, noise, and light, has an

impact on people in many areas of the

world. How to improve the urban

environment and make the world more

livable has become a pressing issue for

the international community (Liu, 2000).

Parks and green spaces could

provide an important role in regards to

environmental protection because they

are endowed with several ecological

functions such as water and soil

conservation, clean air, air conditioning,

noise reduction, and solar radiation

rejection (Xu, Zhuang, Zhang, and Zou,

2001). All of these are beneficial to

improving the quality of life and living

standards as well as benefitting urban

environments. Therefore, the coverage of

parks and green spaces has become a

major indicator for favorable urban

environments.

Urban residents increase their

demand for public green spaces, both in

quantity and quality, as their living

standards improve (Yin, Kong, and Zong,

2008), which requires the local

administrators to provide enough green

space and to design the distribution of the

areas through rational planning. In the

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past, however, the traditional wisdom

often ignored accessibility and fairness

when evaluating the green spaces (Yu and

Wang, 2008), resulting in an

unreasonable and unfair spatial

distribution. The former issue is

illustrated by lower utilization of the

public greenspaces due to their chaotic

arrangement. Many cities in China are

still confronted with the challenge of

having a few parks crowded with people

while the rest are nearly empty (Zhu, Li,

and Chai, 2002). The latter issue is

embodied in inequity; those who live in

wealthy neighborhoods can enjoy the

public greenspaces while those who live

in a slum district are mostly ignored (Lu,

Li, and Yan, 2014). The two problems are

harmful to the sustainable development of

a city and hamper the process of raising

people’s livelihood. Thus, it is valuable to

conduct a quantitative evaluation of the

social service function of the public green

spaces (Yu and Zhong, 2010).

Geographic information systems

(GIS) play an important role in

conducting such an evaluation. GIS

technology possesses a powerful ability

to manage and analyze spatial databases

(Qin and Gao, 2008); that is, it can

provide visual diagrams or tables to show

the results after data collection and

scientific analyses. This study uses GIS to

conduct an analysis and perform

calculations on park and green space data

of central parks in Guilin.

Social Service Function

At present, indicators such as “per capita

public green areas” and “afforestation

coverage in the city” are the main

measures used to design and assess the

social service of public greenspaces in the

international community (Yu and Zhong,

2010). However, these measures cannot

reflect a comprehensive network of urban

public areas. Besides, the public is likely

to pay more attention to factors such as

whether the distribution is reasonable or

if it is effective and fair to all they serve

(Lu, Liu, and Liu, 2011).

Moreover, when calculating the

scope of services, scholars, regardless of

country, will apply the method of setting a

certain radius as the buffer to represent

the service area of the parks and green

spaces (Li and Liu, 2009). This method is

simple and easy but it ignores the

complexity of city traffic and

oversimplifying people’s travel routes. To

make analysis of a service area more

reliable, network analysis can be used to

calculate the service area of the parks and

green spaces (Lian, 2010).

Network analysis is based on real

transportation networks, which has

advantages in calculating accessibility of

service facilities. It can simulate certain

means of transportation to calculate

service area (Jim and Chen, 2003).

Network analysis is used to analyze the

spatial distribution, optimal route, and

service area of limited resources.

In the past, most researchers have

measured the service radius from the

geometric center of the service (Attwell,

2000); however, this method has a

weakness, considering in reality if people

reach the entrance of the parks and green

spaces, they could still enjoy the service

(Cai, Wen, and Cheng, 2011). One of the

advantages of network analysis is

simulation of reality. Network analysis

can not only analyze the accessibility

according to the realistic traffic

conditions and capacity, but also identify

spatial barriers, which makes it more

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optimal for generating service areas

(Zhang, 2012). If the service radius is

measured from the entrances of the parks

or green spaces, where people would

begin to utilize these spaces, the service

area can better reflect reality.

Therefore, this study uses GIS to

delinate the service area of green spaces

while calculating the service coverage

ratio and overlapping ratio to evaluate

accessibility of the public green spaces.

More importantly, through a comparison

of service areas in different residential

land use types, study will examine

inequities in green space distribution.

Methods

Software

The two software programs applied in

this study were Esri’s ArcGIS 10.2 and

Microsoft Excel. The former was used to

create the urban road network, public

green areas, residential areas, input

relevant data, and then analyze the

accessibility and equities of green space

areas. Microsoft Excel was used to

perform calculations on values obtained

from ArcGIS 10.2, and to summarize data

for presention in the form of tables.

Data Collection

The first type of data required was related

to the road network. High-resolution

Google earth imagery of the study area

was used as a base map from which to

generate a road network. This Google

Earth imagery was downloaded from

BIGEMAP software and its coordinate

system consisted of

“Xian_1980_3_Degree_GK_CM_111E”.

The road network was digitized and built

in ArcGIS 10.2 using the imagery as

reference and base map to create digital

road files for mapping.

The second type of data needed

was the location and layout of parks and

green spaces in the city. Data was

obtained from the Guilin Planning Bureau;

parks were classified in five ranks: 5A,

4A, 3A, 2A, and A. Parks were ranked

according to the quality of the green

spaces as related to amenities, diversity,

and other uniquenesses. The higher the

rank, the more attractive the park. The

parks and green spaces data also required

a spatial coordinate system and was

consequently was digitized off of the

aforementioned imagery. The park data

was digitized and attributed for analysis.

The third type of data was related to

land use distribution. Data was obtained

from the Guilin Land and Resources

Bureau. Features came with assigned

values of R1, R2, and R3. R1 refers to

detached houses such as villas. R2 refers

to high-rise blocks of flats. R3 refers to

the areas of mixed use – a mixture of

housing and industry. Figure 1 provides a

layout of assigned property values and

other data within the study area.

Analysis and Results

Network analysis and overlay operations

were conducted in ArcGIS 10.2 and

summarized using Microsoft Excel to

assess the service area distribution. The

service area analysis was an analysis of

accessibility.

Accessibility was interpreted as

an expression of a resident’s desire and

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Figure 1. Foundational data for the analysis. R1 refers to detached houses such as villas. R2 refers to

high-rise blocks of flats. R3 refers to the areas of mixed use – a mixture of housing and industry.

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ability to overcome resistance (such as

distance, time, and cost) to travel to a

facility. It simply determines how

difficult it will be to travel to one place

from another place. A network dataset

was required to help establish road data

for mapping purposes in ArcGIS 10.2.

According to a related study, Zhang (2012)

used different service radii to calculate

service areas according to the different

grades of parks and green spaces (Table

1).

This study used the same radii as

mentioned in Zhang (2012). Using

ArcGIS Network Analyst, entrances of

parks and green spaces were designated

as sources, and the service areas were

calculated with the specified radii

(Table1). Last, all the service areas were

merged (Figure 2). Finally, a coverage

ratio of the green spaces in the city was

calculated (Table 2).

Table 1. Service radius of each grade park.

Class A 2A 3A 4A 5A

Radii

（m） 1000 2000 3000 4000 5000

Table 2. Results of the service area calculation.

Study Area Service Area Percentage

119,762,283 m2 76,099,176 m2 63.54%

The second phase included

intersecting park service areas, which

were calculated on the first step, with

residential land data. The goal of this

phase was to determine which parts of the

residential area fell within and outside of

park service areas. In addition, this phase

help to explore and determine the

proportions of R1, R2, and R3 in the

service area (Figure 3).

The resulting table was exported

into Microsoft Excel to determine the

overall coverage and percentage of land

in the three residential land use types

(Table 3).

Figure 2. Service/study area of the parks and

green spaces.

In addition to the coverage rate,

and in order to ensure equitable access,

city administrators should also consider

the overlap of the park and green space

service areas. Thus, the third phase

consisted of a Union operation; that is, an

overlay analysis was conducted to

determine overlap in the service areas of

the parks and green spaces.

The resulting table was exported

to Microsoft Excel to quantify the

overlapping areas (Table 4). Then,

different colors were used to symbolize

different degrees of overlap on the map

(Figure 4). The darker the color, the

higher the degree of overlap.

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Figure 3. Residential land in the service area.

Table 3. Each residential land use type in the

service area.

Area（m2） Percentage（%）

R1 2,538,154 16.24

R2 7,192,055 46.01

R3 5,902,182 37.76

Total 15,632,391 100

Table 4. Areas and percentages of each overlap.

Degree of

overlap Area（m2） Percentage（%）

1 19,827,892 26.22

2 17,154,595 22.68

3 15,670,539 20.72

4 17,753,941 23.48

5 5,214,304 6.90

For residents, in addition to

thinking about whether they live within

the service area, they also may express

interest about park selections in their

surrounding area. Many within larger

cities value parks and the type of parks or

green spaces. A selection of green spaces

ares provides experiences and variability

for those visiting parks and green spaces.

Figure 4. Overlap of the parks and green spaces.

Darker the color is greater overlap.

The fourth phase was to perform

an Intersect operation and aggregate with

results from prior phases. The goal of this

stage was to obtain the degree of overlap

between the residential land and the

service area.

Results showed the quantity of

available public green spaces in a

residential district. The more overlap, the

more available green spaces. Tables were

then exported into Microsoft Excel to

obtain summary values including area

and percentage within different degrees

of overlap (Table 5; Figure 5).

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Table 5. Each residential land use type in each degree of green space service area overlap.

Figure 5. Overlap of parks and green space in

residential areas. Darker colors = more overlap.

Conclusion

Results of this study identified the

following:

1. The service area and green

coverage in the city.

2. The residential area and

percentage within the service area.

3. Area and percentages of different

degrees of service area.

4. Areas and percentages of service

area overlap in various residential

types.

The social service function is a

significant component of the overall

expectations within the community and

study area. Green spaces provide a level

intangible features and value to society.

Degree of Overlap Type of Residential

Land Area（m2） Percentage（%）

1

R1 100,416 5.39

R2 797,746 42.85

R3 963,603 51.76

Total 1,861,765 100.00

2

R1 291,643 8.63

R2 1,137,992 33.68

R3 1,949,028 57.69

Total 3,378,662 100.00

3

R1 402,973 12.38

R2 1,839,462 56.50

R3 1,012,973 31.12

Total 3,255,408 100.00

4

R1 1,296,586 32.03

R2 1,565,185 38.67

R3 1,185,937 29.30

Total 4,047,708 100.00

5

R1 78,014 10.11

R2 693,371 89.89

R3 11 0.00

Total 771,397 100

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This begs for the appropriate need to

develop a planning system to help

incorporate more parks and green spaces.

When constructing parks and green

spaces, designers should not only be

concerned about their quantity,

accessibility, but distribution should be

taken into consideration as well.

With the help of GIS, this study

helped explore green space and park

accessibility in Guilin. Results suggested

first and foremost, Guilin City has

achieved high green space area coverage;

85.51% of residential areas are located in

the green service areas, although the

green space areas only account for 63.54%

of the entire city. In absence of detailed

population distribution in each

neighborhood, the study assumed an even

distribution of citizens throughout the city.

In other words, this study used the

residential land to green space ratio to

replace the population to green space

ratio.

Second, each green space service

area has a high degree of overlap with

other service areas, suggesting citizens

could enjoy the social service provided

from multiple green spaces. The above

two results suggest the green space areas

in the city appear equitably balanced for

accessibility for citizens in the city.

Third, the residential land ratio in

the service area suggests that the

distribution of green space in the city is

relatively equitable. The proportion of the

R1 residential land use class is

approximately equal to that of the R2 and

R3 land use classes within the bounds of

the city’s green space social service.

Lastly, the overlap of green space

indicates there is little difference in green

space accessibility between those who

live in higher class (R1) districts and

those who live in other districts. The last

two findings seem to support the city is

reasonably fair in providing green areas

to all the residents. The wealthy, who tend

to live in R1 areas (detached houses such

as villas), do not enjoy more convenient

or larger areas of the public green spaces.

Acknowledgements

My deepest gratitude first and foremost

goes to Saint Mary’s University of

Minnesota staff.

Secondly, I also would like to

express my sincere gratitude to other

professors in the School of Foreign

Languages. Without their earnest and

gentle encouragement, this thesis could

not have been completed with such high

quality.

Lastly, I would like to express my

gratitude to my family because of their

wholehearted affection and great

confidence in me all these years. During

the writing process, no matter when I

needed help, they offered me timely

unselfish help.

I also owe my sincere gratitude to

my friends and classmates who spent time

listening to me and inspired me to figure

out my problems during the difficult

course of the thesis.

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